Segmenting Brain Tumors using Alignment-Based Features
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Technical report TR05-19. Detecting and segmenting brain tumors in Magnetic Resonance Images (MRI) is an important but time-consuming task performed by medical experts. Automating this process is a challenging task due to the often high degree of intensity and textural similarity between normal areas and tumor areas. Several recent works have explored aligning a spatial \"template\" image in order to incorporate spatial anatomic information, but it is not obvious how this alignment should be used. This work quantitatively evaluates the performance of 4 different types of Alignment-Based (AB) features encoding spatial anatomic information for use in supervised pixel classification. This is the first work to (1) compare several types of AB features, (2) explore ways to combine different types of AB features, and (3) explore combining AB features with textural features in a learning framework. We considered situations where existing methods perform poorly, and found that combining textural and AB features allows a substantial performance increase, achieving segmentations that very closely resemble expert annotations. | TRID-ID TR05-19
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http://purl.org/coar/resource_type/c_93fc
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en